{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45c4a378",
   "metadata": {},
   "source": [
    "# Задача:\n",
    "#### Провести дисперсионный анализ для определения того, есть ли различия среднего роста среди взрослых футболистов, хоккеистов и штангистов.\n",
    "\n",
    "- Даны значения роста в трех группах случайно выбранных спортсменов: \n",
    "- Футболисты: 173, 175, 180, 178, 177, 185, 183, 182. \n",
    "- Хоккеисты: 177, 179, 180, 188, 177, 172, 171, 184, 180. \n",
    "- Штангисты: 172, 173, 169, 177, 166, 180, 178, 177, 172, 166, 170."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9b23758",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77cfafa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "footballers = np.array([173, 175, 180, 178, 177, 185, 183, 182])\n",
    "hockey_players = np.array([177, 179, 180, 188, 177, 172, 171, 184, 180])\n",
    "weightlifters = np.array([172, 173, 169, 177, 166, 180, 178, 177, 172, 166, 170])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0829cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Выполнение однофакторного ANOVA\n",
    "f_value, p_value = stats.f_oneway(footballers, hockey_players, weightlifters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa522ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-значение: 5.500053450812596\n",
      "p-значение: 0.010482206918698694\n"
     ]
    }
   ],
   "source": [
    "print(\"F-значение:\", f_value)\n",
    "print(\"p-значение:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f31d703b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Есть статистически значимые различия в среднем росте между группами.\n"
     ]
    }
   ],
   "source": [
    "if p_value < 0.05:\n",
    "    print(\"Есть статистически значимые различия в среднем росте между группами.\")\n",
    "else:\n",
    "    print(\"Нет статистически значимых различий в среднем росте между группами.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47e638dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация\n",
    "# Создаем массив с категориями\n",
    "groups = ['Footballers', 'Hockey Players', 'Weightlifters']\n",
    "# Объединяем данные в один список\n",
    "heights = [footballers, hockey_players, weightlifters]\n",
    "# Создаем фигуру и оси для графика\n",
    "fig, ax = plt.subplots()\n",
    "# Добавляем ящики с усами\n",
    "ax.boxplot(heights)\n",
    "# Задаем названия для осей\n",
    "ax.set_xticklabels(groups)\n",
    "ax.set_ylabel('Height (cm)')\n",
    "ax.set_title('Comparison of Heights by Sport')\n",
    "# Отображаем график\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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